First, we evaluated the performance of the DCRNN by studying its capabilities to reproduce empirically observed neural activity patterns, and compared it to a VAR model, like that typically used for the analysis
Causal inferenceElectricity demand forecastingExplainable artificial intelligence (XAI)Graph neural networkInnovative Forecasting Approach:Introduces eXplainable Causal Graph Neural Network (X-CGNN) for electricity demand forecasting, addressing challenges posed by black-box models prevalent in deep neural networks...
First, we evaluated the performance of the DCRNN by studying its capabilities to reproduce empirically observed neural activity patterns, and compared it to a VAR model, like that typically used for the analysis of brain connectivity with Granger causality21,46. We showed that the DCRNN can also ...
a graph attention network based on causal inference named causal graph attention network(C-GAT) is proposed to improve the robustness of the network.The model first calculates the causal weights between the neighborhood of the target node and its label and uses them to sample the neighborhood....
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Following its definition in causal inference, we can further deem this kind of data as an augmentation of the observed data, providing us with an efficient way to confront the data sparsity issue. The relations between the observed data and counterfactual data are shown in Fig. 1(b). The ...
GraphSAGE is a widely-used graph neural network for classification, which generates node embeddings in two steps: sampling and aggregation. In this paper, we introduce causal inference into the GraphSAGE sampling stage, and propose Causal GraphSAGE (C-GraphSAGE) to improve the robustness of the clas...
GNN: graph neural network Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai. Content 1. Survey 2. Models 2.1 Basic Models 2.2 Graph Types 2.3 Pooling Methods 2.4 Analysis 2.5 Efficiency 2.6 Explainability 3. Applications 3.1 Physics 3.2 Chemistry an...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural
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